Évaluation des performances du PMSI pour l’identification des tumeurs incidentes du système nerveux central par rapport à un registre spécialisé en Gironde, France, en 2004

Abstract Background Cancer registries cover 18% of the French population. A national surveillance might be warranted for some potentially environment-related cancers such as tumors of the central nervous system (CNS) to detect abnormal incidence variations. The PMSI database provides an interesting source of comprehensive, standardized and mandatory data collected from all health facilities. The aim of this work was to develop methods to identify incident CNS tumors using the PMSI database. Methods A selection of patients living in Gironde was made in the 2004 PMSI database of the hospital of Bordeaux, using the CNS tumors codification. Cases were validated via the CNS primary tumor registry of Gironde taken as the reference, or medical records. Various combinations of criteria were defined and tested. Results The first selection based on diagnoses identified patients with a sensitivity of 84% and a positive predictive value (PPV) of 34%. Patients wrongly identified by the PMSI were non-incident cases (49%) or patients without a CNS tumor (45%). Patients with a tumor not identified by the PMSI had been hospitalized in 2005 (44%) or had no code for CNS tumor (42%). According to the algorithms, the sensitivity ranged from 64% to 84%, and the PPV from 34% to 69%. The best combination had a sensitivity of 67% and a PPV of 69% and was obtained with codes for CNS tumor in 2004 associated with a diagnostic or therapeutic code for persons under 70 years without code for CNS tumor in previous years or code for metastasis in 2004. Conclusion According to these results, the PMSI database cannot be used alone to calculate the incidence of these complex tumors. However the PMSI database plays an important role in cancer surveillance, in combination with other information sources and the expertise of cancer registries. This role could increase with further reflection and improvement of data quality.